Configurable relevance service test platform

ABSTRACT

In general, embodiments of the present invention provide systems, methods and computer readable media for a configurable test environment within which a relevance service can be invoked to execute one or a combination of test scenarios, each test scenario respectively being configured to exercise one or a combination of features of the relevance service. In embodiments, a test scenario may be configured to use test data that can be simulated and/or be derived from one or a combination of user models and promotion models, and/or be based on aggregated data that has been collected from previous production runs of the relevance service. In embodiments, each test scenario is described as a set of test configuration data. In some embodiments, the test configuration data are represented in a data interchange format that is both human and machine-readable, e.g., JavaScript Object Notation (JSON).

FIELD OF THE INVENTION

Embodiments of the invention relate, generally, to a configurable testenvironment providing verification and validation of a relevanceservice.

BACKGROUND

A relevance service includes systems and methods that are configured toidentify a group of available promotions that are relevant to a user inresponse to receiving a relevance service request on behalf of thatuser. A relevance service may determine relevance of a promotion to auser by matching attributes of the promotion to attributes of the user(e.g., user interests, user location, user gender, and/or user purchasebehavior).

A promotion provider is an example of a business that relies oneffective and timely communication to consumers about promotions thatare available for purchase. The likelihood is increased that aparticular consumer will proceed to purchase a promotion after receivingnotification of available promotions if the provider has the capabilityto identify a subset of available promotions that are most relevant tothe particular consumer and feature those promotions in thenotification. Since communicating effectively with a consumer impactsthe success of a promotion provider's business, it is important to beable to ensure the consistency and accuracy of the system that performsidentification of promotions that are most relevant to the consumer.

Current methods for verification and validation of a relevance serviceexhibit a plurality of problems that make current systems insufficient,ineffective and/or the like. Through applied effort, ingenuity, andinnovation, solutions to improve such methods have been realized and aredescribed in connection with embodiments of the present invention.

SUMMARY

In general, embodiments of the present invention provide herein systems,methods and computer readable media for a configurable test environmentwithin which a relevance service can be invoked to execute one or acombination of test scenarios, each test scenario respectively beingconfigured to exercise one or a combination of features of the relevanceservice. In embodiments, a test scenario may be configured to use testdata that can be simulated and/or be derived from one or a combinationof user models and promotion models, and/or be based on aggregated datathat has been collected from previous production runs of the relevanceservice. In embodiments, each test scenario is described as a set oftest configuration data. In some embodiments, the test configurationdata are represented in a data interchange format that is both human andmachine-readable, e.g., JavaScript Object Notation (JSON).

BRIEF DESCRIPTION OF THE DRAWINGS

Having thus described the invention in general terms, reference will nowbe made to the accompanying drawings, which are not necessarily drawn toscale, and wherein:

FIG. 1 illustrates an example system that can be configured to implementverification and validation of a relevance service in accordance withsome embodiments discussed herein;

FIG. 2 illustrates an example of a test configuration representing anend-to-end test scenario in accordance with some embodiments discussedherein;

FIG. 3 is a flow diagram of an example method 300 for verifying and/orvalidating a relevance service in accordance with some embodimentsdiscussed herein;

FIG. 4 illustrates an example of JSON configuration data representinginput parameters in accordance with some embodiments discussed herein;

FIG. 5 illustrates an example 500 of JSON configuration datarepresenting the expected output data from the relevance service whileprocessing a test scenario in accordance with some embodiments discussedherein;

FIG. 6 is a flow diagram of an example method for generatinguser-specific input data to be used in a test scenario in accordancewith some embodiments discussed herein;

FIG. 7 illustrates an example of JSON configuration data representinguser-specific input parameters in accordance with some embodimentsdiscussed herein;

FIG. 8 illustrates an example of JSON configuration data representinguser-specific attributes that include the user's previous consumerbehavior including past purchases and access status of promotions inaccordance with some embodiments discussed herein;

FIG. 9 is a flow diagram of an example method for generatingpromotion-specific input data to be used in a test scenario inaccordance with some embodiments discussed herein;

FIG. 10 illustrates an example of JSON configuration data representingpromotion-specific input data that are included in test configurationdata describing an end-to-end test scenario in accordance with someembodiments discussed herein;

FIG. 11 illustrates an example of JSON configuration data representingpromotion-specific input data including attributes that describepromotion performance in accordance with some embodiments discussedherein;

FIG. 12 illustrates an example of JSON configuration data representingpromotion-specific input data that include mock search input data inaccordance with some embodiments discussed herein;

FIG. 13 illustrates an example network architecture for a relevanceservice test platform in accordance with some embodiments discussedherein; and

FIG. 14 illustrates a schematic block diagram of circuitry that can beincluded in a computing device, such as a relevance test service module,in accordance with some embodiments discussed herein.

DETAILED DESCRIPTION

The present invention now will be described more fully hereinafter withreference to the accompanying drawings, in which some, but not allembodiments of the inventions are shown. Indeed, these inventions may beembodied in many different forms and should not be construed as limitedto the embodiments set forth herein; rather, these embodiments areprovided so that this disclosure will satisfy applicable legalrequirements. Like numbers refer to like elements throughout.

As used herein, the terms “data,” “content,” “information” and similarterms may be used interchangeably to refer to data capable of beingcaptured, transmitted, received, displayed and/or stored in accordancewith various example embodiments. Thus, use of any such terms should notbe taken to limit the spirit and scope of the disclosure. Further, wherea computing device is described herein to receive data from anothercomputing device, the data may be received directly from the anothercomputing device or may be received indirectly via one or moreintermediary computing devices, such as, for example, one or moreservers, relays, routers, network access points, base stations, and/orthe like. Similarly, where a computing device is described herein tosend data to another computing device, the data may be sent directly tothe another computing device or may be sent indirectly via one or moreintermediary computing devices, such as, for example, one or moreservers, relays, routers, network access points, base stations, and/orthe like.

In embodiments, a relevance service may identify promotions available toa user by using attributes of promotions that are stored in a promotionsrepository and/or attributes of user profile data representing the user.The relevance service may then process that set of promotions forrelevance based on a filtering workflow, which is a combination offiltering algorithms and rules that are applied in sequence to furtherprune the initially identified set of promotions.

In embodiments, the relevance service may weight each identifiedpromotion based its relevance to the user, and then rank the set ofidentified available promotions based on their respective weights asdescribed, for example, in U.S. patent application Ser. No. 13/829,581entitled “Promotion Offering System” and filed on Mar. 14, 2013, whichis incorporated herein in its entirety.

As such, and according to some example embodiments, the systems andmethods described herein are therefore configured to provide aconfigurable test environment within which a relevance service can beinvoked to execute one or a combination of test scenarios, each testscenario respectively being configured to exercise one or a combinationof features of the relevance service.

In embodiments, a test scenario may be configured to use test dataderived from one or a combination of user models and promotion modelsbased on aggregated data that has been collected from previousproduction runs of the relevance service. In embodiments, each testscenario is described as a set of test configuration data.

Examples of test scenarios that may be executed in the test environmentinclude end-to-end test scenarios (i.e., use all relevance servicecomponents in sequence) and directed test scenarios (i.e., use onlyspecified relevance service components) for verification and validationof filtering algorithms and/or algorithm parity; verification ofbusiness rules used in filters, and validation of search results andsearch quality. In embodiments, a test scenario can be based on mockdata (e.g., modeled data and/or data collected from previous productionruns of the relevance service). Additionally and/or alternatively, inembodiments, simulation test scenarios using data describing newpromotions enable use of the test environment as a production monitorfor verification and validation of the new promotion data before theirrelease.

FIG. 1 illustrates an example system 100 that can be configured toimplement verification and validation of a relevance service. System 100comprises a relevance service test platform 110 that includes arelevance test service 120, a test search platform engine 134, and atest relevance indexer engine 136. The relevance test service 120 isconfigured to include a relevance test driver layer 122 that interfacesto at least one application programming interface (API) presented by atleast one component of the relevance service 130. In embodiments, therelevance service 130 includes a relevance engine 132.

The components of system 100 can be communicatively coupled to one ormore of each other. Though the components identified above are describedas being separate or distinct, two or more of the components may becombined into a single process or routine. The functional descriptionprovided herein including separation of responsibility for distinctfunctions is by way of example. Other groupings or other divisions offunctional responsibilities can be made as necessary or in accordancewith design preferences. System 100 further includes communicativecoupling of one or more system components to a test configurationrepository 140, a promotion models source repository 142, a user modelssource repository 144, an offline data sources repository 146, and asearch platform data repository 148.

In embodiments, relevance service 130 may be associated with apromotions provider, and be configured to execute a relevance servicerequest on behalf of a user to identify one or more available promotionsthat are relevant to the user. For example, a relevance service requestmay be generated in response to the user interacting with a page from aweb site that is published by the promotions provider. In anotherexample, the relevance service request may be generated by a searchservice during processing of a search query from a user for promotionshaving particular attributes (e.g., a promotions search query forfamily-friendly restaurants located in San Francisco, Calif.).

In embodiments, a relevance engine 132 may process a relevance requeston behalf of a user by initially identifying the set of all promotionspotentially available to the user and then processing that set ofpromotions for relevance based on a filtering workflow, which is acombination of filtering algorithms and rules that are applied insequence to further prune the initially identified set of promotions.

In embodiments, at least one global filtering algorithm may be the basisfor the initial selection criteria to identify the set of potentiallyavailable promotions. Examples of initial selection criteria includelocation attributes (e.g., only selecting promotions having locationswithin 25 miles of San Francisco) and promotion sold-out status (e.g.,not selecting any promotion associated with a status attribute value of“sold out”).

In embodiments, relevance engine 132 dispatches the identified set ofall potentially available promotions to a filtering workflow for furtherprocessing. In embodiments, the selection of the filtering workflow touse for processing of a relevance request may be based on one or acombination of several criteria. In some embodiments, for example, theselection of a filtering workflow to use for processing a request isbased on a hashing function applied to at least one user attributeand/or one or a combination of attributes associated with the user thatis associated with the request (e.g., user's current location);associated with the request (e.g., current time of day); and/orassociated with the type of request (e.g., a search request).Additionally or alternatively, in some embodiments, the selection of thefiltering workflow to which a request is dispatched is based onaggregated promotion performance data that has been collected from theresults of previously processed user requests (e.g., a current userrequest may be dispatched to a filtering workflow that is associatedwith identifying promotions that, based on previous consumer behavior,have a high likelihood of being purchased by the user).

In embodiments, relevance engine 132 executes a relevance requestreceived from a search platform by returning search results that areranked in terms of the relevance of the promotions represented in theresults. In embodiments, relevance service 130 receives test searchresult promotion data that match the search terms in a test search queryfrom test search platform engine 134. The test search result promotiondata are received from test relevance indexer engine 136, whichretrieves the data from one or more data repositories specified in atest configuration, e.g., promotions models source repository 142 andoffline data sources 146.

In embodiments, relevance service test platform 110 provides aconfigurable test environment within which the relevance service 130 canbe invoked to execute one or a combination of test scenarios, each testscenario respectively being configured to exercise one or a combinationof features of the relevance service 130. In embodiments, a testscenario may be configured to use test data derived from one or acombination of user models and promotion models based on aggregated datathat has been collected from previous production runs of the relevanceservice 130. In embodiments, stored data representing promotion models,user models, and previously collected data from production runs areaccessed by the relevance service test platform 110 from a promotionmodels source repository 142, a user models source repository 144, andan offline data sources repository 146, respectively. In embodiments,each test scenario is described as a set of test configuration data. Insome embodiments, the test configuration data are represented in a datainterchange format that is both human and machine-readable, e.g.,JavaScript Object Notation (JSON). In embodiments, test configurationdata is stored in a test configurations repository 140 that is accessedby the relevance service test platform 110.

In embodiments, test configuration data may describe one of severaltypes of test scenario. For example, a test configuration may describean end-to-end test scenario in which the relevance service 130 processesall components of a relevance service task in response to receiving atest relevance service request. Alternatively, in some embodiments, atest configuration may describe a directed test scenario that exercisesone or a combination of relevance service processing components, e.g.,the scenario specifies processing the test relevance service requestonly using a specified set of filtering workflows.

In some embodiments, a test configuration may describe a mock testscenario based on test input data that is simulated and/or is derivedfrom one or a combination of user models and promotion models, and/or bebased on aggregated data that has been collected from previousproduction runs of the relevance service 130. Thus, a mock test scenariocan be generated to test and/or verify the performance of particularsystem components and/or be used to verify aspects of models that aredeployed or planned for deployment in the production system. Forexample, the input data for a particular scenario may represent a searchfor promotions from pizza restaurants within proximity of Mountain View,Calif. In embodiments, a mock test scenario representing a search forpromotions from pizza restaurants within proximity of another location,e.g., San Francisco, Calif., can be derived from the particular scenarioby modifying the location specified in the test input data.

FIG. 2 illustrates an example of a test configuration 200 representingan end-to-end test scenario. The test configuration 200 is representedas a set of component configuration data JSON files that include theinput parameters 210 to be used in generating the test relevance servicerequest, data representing attributes of a model user 220 on behalf ofwhom the test relevance service request was submitted, data representingthe attributes of a set of model promotions (“deals” in this example)230 potentially available to the model user, and output data 240expected to be returned from the relevance service 130 as a result ofprocessing the test relevance service request. The components of theexample test configuration 200 will be described in more detail belowwith respect to method 300.

FIG. 3 is a flow diagram of an example method 300 for verifying and/orvalidating a relevance service. For convenience, the method 300 will bedescribed with respect to a system that includes one or more computingdevices and performs the method 300. Specifically, the method 300 willbe described with respect to the relevance service test platform 110 ofsystem 100 and, more specifically, with respect to the relevance testservice 120.

In embodiments, the system receives 305 a test configurationrepresenting a test case scenario, e.g., the example test configuration200.

In embodiments, the system generates 310 a test relevance servicerequest based on the test configuration data. The test relevance servicerequest may be generated as a Universal Resource Locator (URL),representing a request that would be generated at a website page inresponse to a user's interaction with that page. Alternatively, the testrelevance service request may be generated as a search request,representing a request generated by a search platform 148 in response tothe search platform 148 receiving a search query from a user.

In some embodiments, the relevance test service 120 includes a relevancetest driver layer 122, and the test relevance service request issubmitted 315 as an invocation of the service request API of therelevance service 130 by a relevance test driver. Directly invoking therelevance service request API facilitates more accurate testing of therelevance service 130, and also enables the relevance test service 120to be modified and scale gracefully as the relevance service 130 designevolves.

In embodiments, the system receives 320 output data generated from therelevance service 130 as a result of processing the test relevanceservice request. In some embodiments, the output data generated by therelevance service 130 include a list of promotions that are ranked basedon their relevance to the user associated with the relevance servicerequest.

In embodiments, the system analyzes 325 the test scenario based on thereceived output data. In some embodiments, the analysis includescomparing the received output data to the expected output data 240specified in the test configuration. The results of the analysis areused for verification and validation of the performance of the relevancesystem.

In some embodiments, relevance service test platform 110 is configuredto support a flexible reporting model that enables generation of one ora combination of several types of reports of test output and/oranalysis. In embodiments, relevance service test platform 110 mayinclude a reporting framework that supports generating a test report inany or all of a variety of reporting formats (e.g., HTML, graphs, andemail) and, additionally or alternatively, generating a test report thatcan be shared among a group of collaborators.

FIG. 4 illustrates an example 400 of JSON configuration datarepresenting the input parameters 210 of test configuration 200. In thisexample, the input parameters represent parameters of an HTTP request tothe relevance service from a model user, and specify data including theuser's email address (“scenario1user@groupon.com”), division(“san-francisco”), and request date (“2012-04-25”). In embodiments, theinput parameters also may include the relevance service API to be calledwhen the request is invoked as well as a request context (“default” inthis example) and other flags for specifying the processing of the testrelevance service request by the relevance service 130.

In embodiments, the input parameters of a test configuration may includeconfigurable flags that specify the type of processing that therelevance service 130 will apply when processing the test relevanceservice request. For example, in some embodiments, the input parametersmay include a configurable blacklist flag that specifies one or morefiltering workflows that are not to be included in the processing of thetest relevance service request. Turning to the example input parameters400, “exclude_ranking_groups” is a flag that specifies a list offiltering workflows that are not to be applied by the relevance service130 during the processing of the test relevance service request that isspecified by the input parameters 400.

In embodiments, a filtering workflow and/or at least one filter within aworkflow may be associated with one or more configurable flags thatmodify processing using the filtering workflow. In some embodiments,each configurable flag has an activation status, and the flag may beconfigured by setting of the flag's activation status. Additionallyand/or alternatively, in some embodiments, an additional configurableflag may be associated with a filtering workflow or a filter duringexecution of a test scenario.

FIG. 5 illustrates an example 500 of JSON configuration datarepresenting the expected output data 240 to be generated by therelevance service 130 while processing the test scenario represented bytest configuration 200. The expected output data include a ranked listof promotions (i.e., “deals”), each promotion being represented by apermalink, and also being associated with a category and with one ormore locations. The expected output data also include a user key 504, acontext 506, and the set of filtering workflows (i.e., the treatment508) that was applied by the relevance service when processing the testrelevance service request.

In some embodiments, a relevance service 130 is configured to includemultiple treatments that potentially could be applied, and the relevanceservice 130 determines which treatment to use in processing a relevanceservice request at the time the request is received. For example, insome embodiments, the selection of a treatment to apply for a submittedrelevance service request is based on one or a combination of selectioncriteria. In embodiments, the selection of a treatment may include, forexample, determining a treatment that matches the result of applying ahash function to at least one attribute (e.g., a user identifier) of theuser on behalf of whom the relevance service request to be processed wassubmitted, determining a treatment that has been assigned to themajority of recently received user requests, determining a treatmentthat was recently added to the configuration, and/or identifying thetreatment that, as a result of A/B testing against another of themultiple treatments, was determined to result in the most successfuloutcomes (e.g., promotion purchases) based on an analysis of consumerresponse to the returned promotions from the two treatments beingtested.

FIG. 6 is a flow diagram of an example method 600 for generatinguser-specific input data to be used in a test scenario. For convenience,the method 600 will be described with respect to a system that includesone or more computing devices and performs the method 600. Specifically,the method 600 will be described with respect to the relevance servicetest platform 110 of system 100 and, more specifically, with respect tothe relevance test service 120.

In embodiments, the system retrieves 605 stored data representing atleast one user model, and then derives 610 user-specific inputparameters based on the user model data.

In embodiments, the system derives user-specific attribute data based ondata representing at least one user model. The user model data is usedto create user-specific attribute data such as email address, location,gender, and data describing user personalization attributes such asthemes and preferences. In some embodiments data representing usermodels is stored in a user models source repository 144.

FIG. 7 illustrates an example 700 of JSON configuration datarepresenting user-specific input parameters 220 that are included in theexample test configuration 200. The user-specific input parametersinclude attributes of the user (e.g., gender) as well as attributes ofthe request associated with the user (e.g., eventType andvalid_until_delta).

FIG. 8 illustrates an example 800 of JSON configuration datarepresenting user-specific attributes that include the user's previousconsumer behavior including past purchases and access status ofpromotions (e.g., “userPurchases”). In embodiments, collected andaggregated data representing previous consumer behavior are stored in anoffline data sources repository, and the system may derive at least someof the user-specific attribute data based on stored data retrieved fromthe offline data sources repository.

FIG. 9 is a flow diagram of an example method 900 for generatingpromotion-specific input data to be used in a test scenario. Forconvenience, the method 900 will be described with respect to a systemthat includes one or more computing devices and performs the method 900.Specifically, the method 900 will be described with respect to therelevance service test platform 110 of system 100 and, morespecifically, with respect to the relevance test service 120.

In embodiments, the system retrieves 905 stored data representing a setof promotion attributes, and then derives 910 promotion-specific inputdata based on the promotion model data. In embodiments,promotion-specific input data may represent one or a combination ofattributes including promotion permalink, promotion type, an identifierof the merchant associated with the promotion, promotion category and/orsubcategory, locations at which the promotion is run, and sold-outstatus of the promotion.

FIG. 10 illustrates an example 1000 of JSON configuration datarepresenting promotion-specific input data 230 that are included in thetest configuration 200. Test configuration 200 is an example of adescription of an end-to-end test scenario. In embodiments, thepromotion-specific input data are the basis for the selection criteriaused by the relevance service 130 in the initial selection of promotionsthat are available to the model user.

FIG. 11 illustrates an example 1100 of JSON configuration datarepresenting promotion-specific input data including attributes thatdescribe promotion performance (e.g., “deal_performance_data”). Inembodiments, collected and aggregated data representing promotionperformance are stored in an offline data sources repository, and thesystem may derive at least some of the promotion-specific attribute databased on stored data retrieved from the offline data sources repository.

FIG. 12 illustrates an example 1200 of JSON configuration datarepresenting promotion-specific input data that include mock searchinput data. As previously described with respect to system 100, arelevance service may receive a relevance service request from a searchplatform 148.

As previously described with respect to method 300, test configurationdata may describe a search relevance test scenario. As illustrated inexample 1200, the promotion attributes represented in thepromotion-specific input data additionally include mock search inputdata that would have been extracted from the search platform 148.

In some embodiments, relevance service test platform 110 is configuredto support delta-based model generation for test configurations. Indelta-based model generation, an existing user and/or promotion modelcan be used as a template for generating a new user and/or promotionmodel. Thus, instead of building the new model from scratch, the newmodel can be generated by incrementally modifying one or a combinationof parameters in the existing model template.

FIG. 13 illustrates example network architecture 1300 for a relevanceservice test platform, which may include one or more devices andsub-systems that are configured to implement some embodiments discussedherein. For example, system 1300 may include relevance test service1310, relevance service 1320, test configurations repository 1340,promotion models source repository 1342, user models source repository1344, offline data sources repository 1346, and search platform 1348.Relevance test service 1310 can include, for example, a relevance testdriver layer (not shown). Relevance service 1320 can include, forexample, relevance engine, search platform engine, and relevance indexerengine (not shown). Relevance test service 1310 and relevance service1320 each respectively can be any suitable network server and/or othertype of processing device. Test configurations repository 1340,promotion models source repository 1342, user models source repository1344, offline data sources repository 1346, and search platform 1348each respectively can be any suitable network database configured tostore data, such as that discussed herein. In this regard relevanceservice test platform 1300 may include, for example, at least onebackend data server, network database, and/or cloud computing device,among other things.

FIG. 14 shows a schematic block diagram of circuitry 1400, some or allof which may be included in, for example, relevance service testplatform 1300. As illustrated in FIG. 14, in accordance with someexample embodiments, circuitry 1400 can include various means, such asprocessor 1402, memory 1404, communications module 1406, and/orinput/output module 1408. As referred to herein, “module” includeshardware, software and/or firmware configured to perform one or moreparticular functions. In this regard, the means of circuitry 1400 asdescribed herein may be embodied as, for example, circuitry, hardwareelements (e.g., a suitably programmed processor, combinational logiccircuit, and/or the like), a computer program product comprisingcomputer-readable program instructions stored on a non-transitorycomputer-readable medium (e.g., memory 1404) that is executable by asuitably configured processing device (e.g., processor 1402), or somecombination thereof.

Processor 1402 may, for example, be embodied as various means includingone or more microprocessors with accompanying digital signalprocessor(s), one or more processor(s) without an accompanying digitalsignal processor, one or more coprocessors, one or more multi-coreprocessors, one or more controllers, processing circuitry, one or morecomputers, various other processing elements including integratedcircuits such as, for example, an ASIC (application specific integratedcircuit) or FPGA (field programmable gate array), or some combinationthereof. Accordingly, although illustrated in FIG. 14 as a singleprocessor, in some embodiments, processor 1402 comprises a plurality ofprocessors. The plurality of processors may be embodied on a singlecomputing device or may be distributed across a plurality of computingdevices collectively configured to function as circuitry 1400. Theplurality of processors may be in operative communication with eachother and may be collectively configured to perform one or morefunctionalities of circuitry 1400 as described herein. In an exampleembodiment, processor 1402 is configured to execute instructions storedin memory 1404 or otherwise accessible to processor 1402. Theseinstructions, when executed by processor 1402, may cause circuitry 1400to perform one or more of the functionalities of circuitry 1400 asdescribed herein.

Whether configured by hardware, firmware/software methods, or by acombination thereof, processor 1402 may comprise an entity capable ofperforming operations according to embodiments of the present inventionwhile configured accordingly. Thus, for example, when processor 1402 isembodied as an ASIC, FPGA or the like, processor 1402 may comprisespecifically configured hardware for conducting one or more operationsdescribed herein. Alternatively, as another example, when processor 1402is embodied as an executor of instructions, such as may be stored inmemory 1404, the instructions may specifically configure processor 1402to perform one or more algorithms and operations described herein, suchas those discussed in connection with FIGS. 3, 6, and 9.

Memory 1404 may comprise, for example, volatile memory, non-volatilememory, or some combination thereof. Although illustrated in FIG. 14 asa single memory, memory 1404 may comprise a plurality of memorycomponents. The plurality of memory components may be embodied on asingle computing device or distributed across a plurality of computingdevices. In various embodiments, memory 1404 may comprise, for example,a hard disk, random access memory, cache memory, flash memory, a compactdisc read only memory (CD-ROM), digital versatile disc read only memory(DVD-ROM), an optical disc, circuitry configured to store information,or some combination thereof. Memory 1404 may be configured to storeinformation, data (including analytics data), applications,instructions, or the like for enabling circuitry 1400 to carry outvarious functions in accordance with example embodiments of the presentinvention. For example, in at least some embodiments, memory 1404 isconfigured to buffer input data for processing by processor 1402.Additionally or alternatively, in at least some embodiments, memory 1404is configured to store program instructions for execution by processor1402. Memory 1404 may store information in the form of static and/ordynamic information. This stored information may be stored and/or usedby circuitry 1400 during the course of performing its functionalities.

Communications module 1406 may be embodied as any device or meansembodied in circuitry, hardware, a computer program product comprisingcomputer readable program instructions stored on a computer readablemedium (e.g., memory 1404) and executed by a processing device (e.g.,processor 1402), or a combination thereof that is configured to receiveand/or transmit data from/to another device, such as, for example, asecond circuitry 1400 and/or the like. In some embodiments,communications module 1406 (like other components discussed herein) canbe at least partially embodied as or otherwise controlled by processor1402. In this regard, communications module 1406 may be in communicationwith processor 1402, such as via a bus. Communications module 1406 mayinclude, for example, an antenna, a transmitter, a receiver, atransceiver, network interface card and/or supporting hardware and/orfirmware/software for enabling communications with another computingdevice. Communications module 1406 may be configured to receive and/ortransmit any data that may be stored by memory 1404 using any protocolthat may be used for communications between computing devices.Communications module 1406 may additionally or alternatively be incommunication with the memory 1404, input/output module 1408 and/or anyother component of circuitry 1400, such as via a bus.

Input/output module 1408 may be in communication with processor 1402 toreceive an indication of a user input and/or to provide an audible,visual, mechanical, or other output to a user. Some example visualoutputs that may be provided to a user by circuitry 1400 are discussedin connection with FIG. 1. As such, input/output module 1408 may includesupport, for example, for a keyboard, a mouse, a joystick, a display, atouch screen display, a microphone, a speaker, a RFID reader, barcodereader, biometric scanner, and/or other input/output mechanisms. Inembodiments wherein circuitry 1400 is embodied as a server or database,aspects of input/output module 1408 may be reduced as compared toembodiments where circuitry 1400 is implemented as an end-user machineor other type of device designed for complex user interactions. In someembodiments (like other components discussed herein), input/outputmodule 1408 may even be eliminated from circuitry 1400. Alternatively,such as in embodiments wherein circuitry 1400 is embodied as a server ordatabase, at least some aspects of input/output module 1408 may beembodied on an apparatus used by a user that is in communication withcircuitry 1400, such as for example, pharmacy terminal 108. Input/outputmodule 1408 may be in communication with the memory 1404, communicationsmodule 1406, and/or any other component(s), such as via a bus. Althoughmore than one input/output module and/or other component can be includedin circuitry 1400, only one is shown in FIG. 14 to avoidovercomplicating the drawing (like the other components discussedherein).

Relevance test service module 1410 may also or instead be included andconfigured to perform the functionality discussed herein related to theverification and validation of a relevance service discussed above. Insome embodiments, some or all of the functionality of verification andvalidation may be performed by processor 1402. In this regard, theexample processes and algorithms discussed herein can be performed by atleast one processor 1402 and/or relevance test service module 1410. Forexample, non-transitory computer readable media can be configured tostore firmware, one or more application programs, and/or other software,which include instructions and other computer-readable program codeportions that can be executed to control each processor (e.g., processor1402 and/or relevance test service module 1410) of the components ofsystem 400 to implement various operations, including the examples shownabove. As such, a series of computer-readable program code portions areembodied in one or more computer program products and can be used, witha computing device, server, and/or other programmable apparatus, toproduce machine-implemented processes.

Any such computer program instructions and/or other type of code may beloaded onto a computer, processor or other programmable apparatus'scircuitry to produce a machine, such that the computer, processor otherprogrammable circuitry that execute the code on the machine create themeans for implementing various functions, including those describedherein.

It is also noted that all or some of the information presented by theexample displays discussed herein can be based on data that is received,generated and/or maintained by one or more components of system 1300. Insome embodiments, one or more external systems (such as a remote cloudcomputing and/or data storage system) may also be leveraged to provideat least some of the functionality discussed herein.

As described above, aspects of embodiments of the present invention maybe configured as methods, mobile devices, backend network devices, andthe like. Accordingly, embodiments may comprise various means includingentirely of hardware or any combination of software and hardware.Furthermore, embodiments may take the form of a computer program producton at least one non-transitory computer-readable storage medium havingcomputer-readable program instructions (e.g., computer software)embodied in the storage medium. Any suitable computer-readable storagemedium may be utilized including non-transitory hard disks, CD-ROMs,flash memory, optical storage devices, or magnetic storage devices.

Embodiments of the present invention have been described above withreference to block diagrams and flowchart illustrations of methods,apparatuses, systems and computer program products. It will beunderstood that each block of the circuit diagrams and process flowdiagrams, and combinations of blocks in the circuit diagrams and processflowcharts, respectively, can be implemented by various means includingcomputer program instructions. These computer program instructions maybe loaded onto a general purpose computer, special purpose computer, orother programmable data processing apparatus, such as processor 1402and/or relevance test service module 1410 discussed above with referenceto FIG. 14, to produce a machine, such that the computer program productincludes the instructions which execute on the computer or otherprogrammable data processing apparatus create a means for implementingthe functions specified in the flowchart block or blocks.

These computer program instructions may also be stored in acomputer-readable storage device (e.g., memory 1404) that can direct acomputer or other programmable data processing apparatus to function ina particular manner, such that the instructions stored in thecomputer-readable storage device produce an article of manufactureincluding computer-readable instructions for implementing the functiondiscussed herein. The computer program instructions may also be loadedonto a computer or other programmable data processing apparatus to causea series of operational steps to be performed on the computer or otherprogrammable apparatus to produce a computer-implemented process suchthat the instructions that execute on the computer or other programmableapparatus provide steps for implementing the functions discussed herein.

Accordingly, blocks of the block diagrams and flowchart illustrationssupport combinations of means for performing the specified functions,combinations of steps for performing the specified functions and programinstruction means for performing the specified functions. It will alsobe understood that each block of the circuit diagrams and processflowcharts, and combinations of blocks in the circuit diagrams andprocess flowcharts, can be implemented by special purpose hardware-basedcomputer systems that perform the specified functions or steps, orcombinations of special purpose hardware and computer instructions

Many modifications and other embodiments of the inventions set forthherein will come to mind to one skilled in the art to which theseinventions pertain having the benefit of the teachings presented in theforegoing descriptions and the associated drawings. Therefore, it is tobe understood that the inventions are not to be limited to the specificembodiments disclosed and that modifications and other embodiments areintended to be included within the scope of the appended claims.Although specific terms are employed herein, they are used in a genericand descriptive sense only and not for purposes of limitation.

What is claimed is:
 1. A relevance test system for executing one or acombination of test scenarios, each test scenario respectively beingconfigured to selectively exercise components of a relevance service,the system comprising: the relevance service configured to provide aprogram interface for receiving relevance service requests via anetwork, wherein each relevance service request is associated with auser, and the relevance service is configured to process the relevanceservice request by identifying a set of promotions that are relevant tothe user using components of the relevance service in a sequence,wherein the components include predefined filtering algorithms andfiltering rules; and a relevance test service platform comprising arelevance test driver layer, wherein the relevance test service platformis operable to interact with the relevance service using the programinterface via the network using at least one relevance test driver fromthe relevance test driver layer, the relevance test service platformbeing configured to perform operations comprising: receiving testconfiguration data describing a test scenario representing processing ofa test relevance service request on behalf of at least one model user,wherein: the test relevance service request is submitted as a directinvocation of the program interface of the relevance service by the atleast one relevance test driver from the relevance test driver layer;the test configuration data includes filtering workflow parametersdefining the test scenario as one of (a) an end-to-end test of thecomponents of the relevance service in the sequence or (b) a directedtest of a selected one or more components of the relevance service; thetest configuration data further includes model user input parametersdescribing the model user and model promotion input parametersdescribing one or more promotion attributes for selection by therelevance service of a set of promotions potentially available to themodel user, wherein the model user input parameters comprise at least auser email address, a user location, a user gender, data representinguser consumer behavior, and data representing user-personalized themesand preferences; and the test configuration data further includes afirst output expected to be generated by the relevance service whileprocessing the test relevance service request for the end-to-end test orthe directed test; and in response to receiving the test configurationdata: generating the test relevance service request based on the testconfiguration data; submitting the test relevance service request to therelevance service using the relevance service program interface;receiving a second output generated by the relevance service whileprocessing the test relevance service request; and analyzing the testscenario based in part on comparing the second output to the firstoutput.
 2. The system of claim 1, wherein the model user inputparameters are derived from user model source data, and deriving themodel user input parameters comprises: retrieving stored user modelsource data from a user models source repository; and deriving the modeluser input parameters based on the stored user model source data.
 3. Thesystem of claim 1, wherein the model promotion input parameters includeat least one of promotion permalink, promotion type, promotion merchantidentifier, promotion category, promotion subcategory, locations atwhich the promotion is offered, promotion sold-out status, and promotionperformance data.
 4. The system of claim 3, wherein the model promotioninput parameters are derived from promotion model source data, andderiving the model promotion input parameters comprises: retrievingstored promotion model source data from a promotion models sourcerepository; and deriving the model promotion input parameters based onthe stored promotion model source data.
 5. The system of claim 1,wherein at least one filtering rule of the filtering rules is a businessrule.
 6. The system of claim 1, wherein the filtering workflowparameters include at least one configurable flag that defines theselected one or more components of the directed test, wherein the atleast one configurable flag has an activation status, and wherein thefiltering workflow parameters include a setting for the activationstatus of the at least one configurable flag.
 7. The system of claim 1,wherein the test relevance service request is a search request, andwherein the model promotion input parameters include at least onepromotion search attribute.
 8. The system of claim 1, wherein the testconfiguration data are represented in a data interchange format.
 9. Thesystem of claim 8, wherein the test configuration data are stored in atest configurations repository, and wherein receiving the testconfiguration data representing a test scenario is preceded byretrieving the stored test configuration data from the testconfigurations repository.
 10. A computer-implemented method forexecuting one or a combination of test scenarios, each test scenariorespectively being configured to selectively exercise components of arelevance service, the method comprising, by one or more processors of auniversal relevance test service: receiving, by a relevance test serviceplatform comprising a relevance test driver layer, test configurationdata describing a test scenario representing processing of a testrelevance service request on behalf of at least one model user, wherein:the test relevance service request is submitted as a direct invocationof a program interface of the relevance service by at least onerelevance test driver from the relevance test driver layer the relevanceservice is configured to process the relevance service request receivedvia a network by identifying a set of promotions that are relevant tothe at least one user model using components of the relevance service ina sequence; the components of the relevance service include predefinedfiltering algorithms and filtering rules; the test configuration dataincludes filtering workflow parameters defining the test scenario as oneof (a) an end-to-end test of the components of the relevance service inthe sequence or (b) a directed test of a selected one or more componentsof the relevance service; the test configuration data further includesmodel user input parameters describing the model user and modelpromotion input parameters describing one or more promotion attributesfor selection by the relevance service of a set of promotionspotentially available to the model user, wherein the model user inputparameters comprise at least a user email address, a user location, auser gender, data representing user consumer behavior, and datarepresenting user-personalized themes and preferences; and the testconfiguration data further includes a first output expected to begenerated by the relevance service while processing the test relevanceservice request for the end-to-end test or the directed test; and inresponse to receiving the test configuration data: generating the testrelevance service request based on the test configuration data;submitting the test relevance service request to the relevance servicevia the network; receiving a second output generated by the relevanceservice while processing the test relevance service request; andanalyzing the test scenario based in part on comparing the second outputto the first output.
 11. The method of claim 10, wherein the model userinput parameters are derived from user model source data, and derivingthe model user input parameters comprises: retrieving stored user modelsource data from a user models source repository; and deriving the modeluser input parameters based on the stored user model source data. 12.The method of claim 10, wherein the model promotion input parametersinclude at least one of promotion permalink, promotion type, promotionmerchant identifier, promotion category, promotion subcategory,locations at which the promotion is offered, promotion sold-out status,and promotion performance data.
 13. The method of claim 12, wherein themodel promotion input parameters are derived from promotion model sourcedata, and deriving the model promotion input parameters comprises:retrieving stored promotion model source data from a promotion modelssource repository; and deriving the model promotion input parametersbased on the stored promotion model source data.
 14. The method of claim10, wherein the filtering workflow parameters include at least oneconfigurable flag that defines the selected one or more components ofthe directed test, wherein the at least one configurable flag has anactivation status, and wherein the filtering workflow parameters includea setting for the activation status of the at least one configurableflag.
 15. The method of claim 10, wherein the test relevance servicerequest is a search request, and wherein the model promotion inputparameters include at least one promotion search attribute.
 16. Themethod of claim 10, wherein the test configuration data are representedin a data interchange format.
 17. The method of claim 16, wherein thetest configuration data are stored in a test configurations repository,and wherein receiving the test configuration data representing a testscenario is preceded by retrieving the stored test configuration datafrom the test configurations repository.
 18. A non-transitory computerreadable medium for implementing a relevance test system executing oneor a combination of test scenarios, each test scenario respectivelybeing configured to selectively exercise one or a components of arelevance service, the computer readable medium including instructionsthat when executed by one or more processors configures the one or moreprocessors to: provide a relevance service configured to provide aprogram interface for receiving relevance service requests via anetwork, wherein each relevance service request is associated with auser, and the relevance service is configured to process the relevanceservice request by identifying a set of promotions that are relevant tothe user using components of the relevance service in a sequence,wherein the components include predefined filtering algorithms andfiltering rules; and provide a relevance test service platformcomprising a relevance test driver layer, wherein the relevance testservice platform is configured to interact with the relevance serviceusing the program interface via the network using at least one relevancetest driver from the relevance test driver layer based on: receivingtest configuration data describing a test scenario representingprocessing of a test relevance service request on behalf of at least onemodel user, wherein: the test relevance service request is submitted asa direct invocation of a the program interface of the relevance serviceby the at least one relevance test driver from the relevance test driverlayer; the test configuration data includes filtering workflowparameters defining the test scenario as one of (a) an end-to-end testof the components of the relevance service in the sequence or (b) adirected test of a selected one or more components of the relevanceservice; the test configuration data further includes model user inputparameters describing the model user and model promotion inputparameters describing one or more promotion attributes for selection bythe relevance service of a set of promotions potentially available tothe model user, wherein the model user input parameters comprise atleast a user email address, a user location, a user gender, datarepresenting user consumer behavior, and data representinguser-personalized themes and preferences; and the test configurationdata further includes a first output expected to be generated by therelevance service while processing the test relevance service requestfor the end-to-end test or the directed test; and in response toreceiving the test configuration data: generating the test relevanceservice request based on the test configuration data; submitting thetest relevance service request to the relevance service using therelevance service program interface; receiving a second output generatedby the relevance service while processing the test relevance servicerequest; and analyzing the test scenario based in part on comparing thesecond output to the first output.